Explain ML model fundamentals evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Question
Explain the principles and assumptions behind logistic regression. How does Naive Bayes work and when does it perform well? Describe the transformer architecture and why self-attention helps. What metrics would you use to evaluate a multi-class classification model and why? Compare bagging and boosting: how do they reduce error?
Quick Answer: Explain ML model fundamentals evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.